Data Science Blog

Our team is often asked why machine learning (ML) isn’t more prevalent in healthcare. In this first post of a series on barriers to healthcare ML, we discuss one of the biggest hurdles, data reliability. Most CTOs in the health space are excited about using ML throughout their healthcare systems if they aren’t already. We hear stories of neural nets outperforming seasoned clinicians in prominent research studies, such as predicting mortality based solely off of when lab tests were ordered, or predicting patient characteristics from retinal images. That…

We are thrilled to announce the release of version 2.0 of our R package, healthcare.ai. The goal of the software is to make it as easy and fast as possible to put machine learning models to work for health systems. We overhauled the code for this release to make the package even easier to use, to automatically avert problems that commonly arise in machine learning deployments, and to boost models’ predictive power. This post describes how the package does that, but if you’re more of a hands-on type,…

­­­­We started healthcare.ai in late 2016 to bring machine learning (ML) to the healthcare masses. As we release version 2.0 of the software (on April 20th), it’s worth stepping back to fully understand why we invest in this open-source project, which is freely available to all. Why would a for-profit firm spend time investing in this public good? Since the 2009 HITECH act incentivized EHR adoption, data has become much more ubiquitous in healthcare. Despite all that’s gone wrong in US healthcare, the fact that healthcare data is…

Many vendors deliver machine learning models with different applications in healthcare. But they don’t all deliver accurate models that are easy to implement, targeted to a specific use case, connected to actionable interventions, and surrounded by a machine learning community and support team with extensive, exclusive healthcare experience.
These machine learning qualities are possible only through a machine learning model delivered by a vendor with a unique set of capabilities. There are five differentiators behind effective machine learning models and vendors:

Vendor’s expertise and exclusive focus on healthcare.

Machine learning model’s access to extensive data sources.

Machine learning model’s ease of implementation.

Machine learning model’s interpretability and buy-in.

Machine learning model’s conformance with privacy standards.

These five factors separate the high-value vendors and models from the crowd, so healthcare systems can quickly implement machine learning and start seeing improvement results.

tl;dr: Healthcare needs practical machine learning tools; the focus on deep learning and GPUs doesn’t help the average health system. Background Google just released a paper called “Scalable and accurate deep learning for electronic health records” that has received deserved acclaim in both the machine learning (ML) and healthcare communities. This research comes from the Google Brain group and isn’t their first foray into healthcare. See, for example, their impressive work in diabetic retinopathy. In fact, it’s now common for tech giants to wade into…

Loading Contents...

Subscribe and get updates delivered to your email.

This project was started by and receives ongoing support from Health Catalyst.